Assessing the Potential Effect of Programmatic Changes in Medicaid and SCHIP on Children’s Uninsured Rates

Matthew M. Davis, MD, MAPP

Rachel M. Quinn, MPP, MPH

With support from the Michigan Department of Community Health

CHEAR Unit, Division of General Pediatrics, and the Gerald R. Ford School of Public Policy, University of Michigan

                                                                                            

                  

How Low Can Child Uninsurance Rates Go?

• Political opportunities

• Fiscal realities

• Programmatic options?

Focus on Individual-Level Determinants of Uninsurance

• Clinician’s perspective– Causes– Effects

• Anecdotally powerful

Focus on Individual-Level Determinants of Uninsurance

• Clinician’s perspective– Causes– Effects

• Anecdotally powerful

• But what about programmatic opportunities at state and federal levels?

Research Question

What are sociodemographic and programmatic factors at the state level associated with rates

of uninsurance among children?

Program Opportunities in Context of Population Factors

• Candidate sociodemographic and programmatic factors at the state level associated with rates of uninsurance among children– Sociodemographic

• Race/ethnicity, immigration, median income, unemployment rates, employer insurance offer rates, population age balance

– Programmatic• Medicaid and SCHIP income eligibility thresholds,

asset tests, copays/premiums for SCHIP, SCHIP program type

State-to-State Comparison of Child Uninsurance Rates

• Current Population Survey (CPS)– March (“Sociodemographic”) Supplement– Annual household survey– Nationally representative– Representative estimates for all states and DC– 2000 – 2004 (rates from 1999-2003)

State Data Regarding Candidate Uninsurance Factors

• Census data• Bureau of Labor Statistics• Centers for Medicare and Medicaid Services• Foundation reports

– Kaiser Family Foundation StateFacts– Center for Budget and Policy Priorities

Data Analysis

• Time-series analysis– Generalized estimating equations– Within each state (1999-2003)– Between states– Bivariate analyses Multivariate analyses– Adjust for different state populations

Methodologic Options and Challenges

• Outcomes– For all children– For low-income children

• Collinearity of independent variables– e.g., Income eligibility levels for different child age

groups within Medicaid• Necessitated “families” of models with

interchanging collinear variables

Results: Uninsurance Rates for All Children

• Variables significant in bivariate tests included:– Sociodemographic variables:

• Median income• Proportion of state population who are Hispanic• Proportion of state population who are children

– Programmatic variables:• Asset test• SCHIP income eligibility thresholds• Medicaid income eligibility thresholds

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income

Prop. Hispanic

Prop. children

No asset test

SCHIP elig thresh

M’caid elig 0-1

M’caid elig 2-5

M’caid elig 6-16

M’caid elig 17-19

*P<.0001; ‡P<.05

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income -.0002*

Prop. Hispanic .261*

Prop. children .390*

No asset test

SCHIP elig thresh

M’caid elig 0-1

M’caid elig 2-5

M’caid elig 6-16

M’caid elig 17-19

*P<.0001; ‡P<.05

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income -.0002* -.0001*

Prop. Hispanic .261* .243*

Prop. children .390* .347*

No asset test -.644

SCHIP elig thresh -.012 ‡

M’caid elig 0-1 -.012

M’caid elig 2-5

M’caid elig 6-16

M’caid elig 17-19

*P<.0001; ‡P<.05

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income -.0002* -.0001* -.0002*

Prop. Hispanic .261* .243* .248*

Prop. children .390* .347* .353*

No asset test -.644 -.744

SCHIP elig thresh -.012 ‡ -.013 ‡

M’caid elig 0-1 -.012

M’caid elig 2-5 -.009

M’caid elig 6-16

M’caid elig 17-19

*P<.0001; ‡P<.05

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income -.0002* -.0001* -.0002* -.0001*

Prop. Hispanic .261* .243* .248* .247*

Prop. children .390* .347* .353* .352*

No asset test -.644 -.744 -.697

SCHIP elig thresh -.012 ‡ -.013 ‡ -.012 ‡

M’caid elig 0-1 -.012

M’caid elig 2-5 -.009

M’caid elig 6-16 -.009

M’caid elig 17-19

*P<.0001; ‡P<.05

Models of Uninsurance Rates for All Children

Model 1 Model 2 Model 3 Model 4 Model 5

Median income -.0002* -.0001* -.0002* -.0001* -.0001*

Prop. Hispanic .261* .243* .248* .247* .245*

Prop. children .390* .347* .353* .352* .348*

No asset test -.644 -.744 -.697 -.580

SCHIP elig thresh -.012 ‡ -.013 ‡ -.012 ‡ -.011 ‡

M’caid elig 0-1 -.012

M’caid elig 2-5 -.009

M’caid elig 6-16 -.009

M’caid elig 17-19 -.010 ‡

*P<.0001; ‡P<.05

Models of Uninsurance Rates for Low-income Children

Model 1 Model 2 Model 3 Model 4 Model 5

Prop. Hispanic .349* .320* .329* .329* .321*

Prop. children .642 ‡ .225 .285 .274 .293

No asset test -2.58 -2.90 -2.83 -2.50

SCHIP elig thresh -.010 -.012 -.010 -.006

M’caid elig 0-1 -.036 ‡

M’caid elig 2-5 -.032 ‡

M’caid elig 6-16 -.030 ‡

M’caid elig 17-19 -.033 ‡

*P<.0001; ‡P<.01; also adjusted for type of SCHIP program

Limitations

• CPS data not equivalently accurate for all states– Larger states likely with better estimates

• Much variation in child uninsurance rates remains unexplained by state-level variables– Opportunity for multi-level model of likelihood of

uninsurance for a child, given individual, family, community, and state-level variables

– Influence of state variables likely varies across states

Summary

• State-level model consistent with individual-level factors associated with uninsurance– Income– Hispanic ethnicity

• Consistent with hypothesized program effects– Eligibility thresholds– Asset test

• New insight– Proportion of state population comprised by

children

Implication: Eliminate the Asset Test

Implication: Eliminate the Asset Test

• But only 6 states still have an asset test– CO, ID, MT, NV, TX, UT

Implication: Modify Medicaid Eligibility Thresholds

If raise Medicaid eligibility threshold to:

Estimated child uninsurance rate

133% 8.7% - 9.1%

150% 8.1% - 8.7%

185% 6.7% - 7.8%

200% 6.2% - 7.4%

If State X has Medicaid eligibility threshold of 100% FPL and a low-income child uninsurance rate of 10% …

Implication: Modify SCHIP Eligibility Thresholds

If raise SCHIP eligibility threshold to:

Estimated child uninsurance rate

200% 9.8% - 9.9%

235% 9.2% - 9.3%

250% 9.0% - 9.2%

300% 8.3% - 8.5%

If State Y has SCHIP eligibility threshold of 185% FPL and an overall child uninsurance rate of 10% …

Implication: Consider the State Proportion of Children

• Range of states’ proportions of population comprised by children:– High

– Low

Implication: Consider the State Proportion of Children

• Range of states’ proportions of population comprised by children:– High

• UT 32.6%• AK 30.1%

– Low

Implication: Consider the State Proportion of Children

• Range of states’ proportions of population comprised by children:– High

• UT 32.6%• AK 30.1%

– Low• ME 22.4%• DC 19.7%

Implication: Consider the State Proportion of Children

• Range of states’ proportions of population comprised by children:– High Child uninsurance rate

• UT 32.6% 9.0%• AK 30.1% 12.3%

– Low• ME 22.4% 6.0%• DC 19.7% 11.4%

Conclusions

• Value of considering child uninsurance within the state context

• Opportunities to use models to inform legislators and policymakers about possible yields of program changes

• New insights about possible factors for consideration in federal match rate